EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms - Zhang, Ran; Sui, Linfeng; Gong, Jinming; Cao, Jianting (Frontiers in Physiology, 2023)
In real-time electroencephalography (EEG) data analysis, the problem of observing dynamic changes and binary classification problems is a promising direction are very important to achieve real-time EEG analysis. To address these problems, we proposed two algorithms: dynamic turning tangent empirical mode decomposition (D-2TEMD) to compute EEG energy, and dynamic approximate entropy (D-ApEn) to compute EEG complexity. Experimental results demonstrated the effectiveness of D-2TEMD and D-ApEn in the classification of coma and brain death. Finally, by applying the two algorithms and a graphical user interface (GUI) design tool, the real-time diagnostic system is implemented.